scholarly journals Helly-Type Theorems in Property Testing

2018 ◽  
Vol 28 (04) ◽  
pp. 365-379
Author(s):  
Sourav Chakraborty ◽  
Rameshwar Pratap ◽  
Sasanka Roy ◽  
Shubhangi Saraf
Keyword(s):  
High Probability ◽  
Convex Sets ◽  
Property Testing ◽  
Geometric Object ◽  
Fundamental Result ◽  
Symmetric Convex ◽  
Helly’S Theorem ◽  
Convex Object ◽  
Constant Size ◽  
Testing Algorithm

Helly’s theorem is a fundamental result in discrete geometry, describing the ways in which convex sets intersect with each other. If [Formula: see text] is a set of [Formula: see text] points in [Formula: see text], we say that [Formula: see text] is [Formula: see text]-clusterable if it can be partitioned into [Formula: see text] clusters (subsets) such that each cluster can be contained in a translated copy of a geometric object [Formula: see text]. In this paper, as an application of Helly’s theorem, by taking a constant size sample from [Formula: see text], we present a testing algorithm for [Formula: see text]-clustering, i.e., to distinguish between the following two cases: when [Formula: see text] is [Formula: see text]-clusterable, and when it is [Formula: see text]-far from being [Formula: see text]-clusterable. A set [Formula: see text] is [Formula: see text]-far [Formula: see text] from being [Formula: see text]-clusterable if at least [Formula: see text] points need to be removed from [Formula: see text] in order to make it [Formula: see text]-clusterable. We solve this problem when [Formula: see text], and [Formula: see text] is a symmetric convex object. For [Formula: see text], we solve a weaker version of this problem. Finally, as an application of our testing result, in the case of clustering with outliers, we show that with high probability one can find the approximate clusters by querying only a constant size sample.

On Gaussian Correlation Inequalities for Rectangles and Symmetric Sets

2002 ◽  
Vol 53 (3-4) ◽  
pp. 245-248
Author(s):  
Subir K. Bhandari ◽  
Ayanendranath Basu
Keyword(s):  
Recent Result ◽  
Convex Sets ◽  
Convex Set ◽  
Correlation Inequalities ◽  
The Other ◽  
Symmetric Convex ◽  
Complete Proof ◽  
Other Hand

Pitt's conjecture (1977) that P( A ∩ B) ≥ P( A) P( B) under the Nn (0, In) distribution of X, where A, B are symmetric convex sets in IRn still lacks a complete proof. This note establishes that the above result is true when A is a symmetric rectangle while B is any symmetric convex set, where A, B ∈ IRn. We give two different proofs of the result, the key component in the first one being a recent result by Hargé (1999). The second proof, on the other hand, is based on a rather old result of Šidák (1968), dating back a period before Pitt's conjecture.

scholarly journals Some inequalities for symmetric convex sets with applications

1996 ◽  
Vol 24 (2) ◽  
pp. 753-762 ◽  
Author(s):  
T. W. Anderson
Keyword(s):  
Convex Sets ◽  
Symmetric Convex

scholarly journals Eckhoff's Problem on Convex Sets in the Plane

10.37236/9978 ◽  
2021 ◽  
Vol 28 (3) ◽  
Author(s):  
Adam S. Jobson ◽  
André E. Kézdy ◽  
Jenő Lehel
Keyword(s):  
Convex Sets ◽  
Common Point ◽  
Finite Family ◽  
Helly’S Theorem ◽  
Uniform Hypergraphs ◽  
Special Cases

Eckhoff proposed a combinatorial version of the classical Hadwiger–Debrunner $(p,q)$-problems as follows. Let ${\cal F}$ be a finite family of convex sets in the plane and  let $m\geqslant 1$ be an integer. If among every ${m+2\choose 2}$ members of ${\cal F}$ all but at most $m-1$ members have a common point, then there is a common point for all but at most $m-1$ members of ${\cal F}$. The claim is an extension of Helly's theorem ($m=1$). The case $m=2$ was verified by Nadler and by Perles. Here we show that Eckhoff 's conjecture follows from an old conjecture due to Szemerédi and Petruska concerning $3$-uniform hypergraphs. This conjecture is still open in general; its  solution for a few special cases answers Eckhoff's problem for $m=3,4$. A new proof for the case $m=2$ is also presented.

scholarly journals Large Cliques in a Power-Law Random Graph

2010 ◽  
Vol 47 (4) ◽  
pp. 1124-1135 ◽  
Author(s):  
Svante Janson ◽  
Tomasz Łuczak ◽  
Ilkka Norros
Keyword(s):  
Random Graph ◽  
Power Law ◽  
High Probability ◽  
Simple Algorithm ◽  
Heavy Tail ◽  
Degree Sequences ◽  
Constant Size ◽  
Tail Distribution ◽  
Law Degree ◽  
Large Clique

In this paper we study the size of the largest clique ω(G(n, α)) in a random graph G(n, α) on n vertices which has power-law degree distribution with exponent α. We show that, for ‘flat’ degree sequences with α > 2, with high probability, the largest clique in G(n, α) is of a constant size, while, for the heavy tail distribution, when 0 < α < 2, ω(G(n, α)) grows as a power of n. Moreover, we show that a natural simple algorithm with high probability finds in G(n, α) a large clique of size (1 − o(1))ω(G(n, α)) in polynomial time.

scholarly journals Quantitative anisotropic isoperimetric and Brunn−Minkowski inequalities for convex sets with improved defect estimates

2018 ◽  
Vol 24 (2) ◽  
pp. 479-494
Author(s):  
Davit Harutyunyan
Keyword(s):  
Convex Sets ◽  
Space Dimension ◽  
Arithmetic Mean ◽  
Symmetric Convex ◽  
Mass Transportation ◽  
Best Constant ◽  
Centrally Symmetric

In this paper we revisit the anisotropic isoperimetric and the Brunn−Minkowski inequalities for convex sets. The best known constant C(n) = Cn7 depending on the space dimension n in both inequalities is due to Segal [A. Segal, Lect. Notes Math., Springer, Heidelberg 2050 (2012) 381–391]. We improve that constant to Cn6 for convex sets and to Cn5 for centrally symmetric convex sets. We also conjecture, that the best constant in both inequalities must be of the form Cn2, i.e., quadratic in n. The tools are the Brenier’s mapping from the theory of mass transportation combined with new sharp geometric-arithmetic mean and some algebraic inequalities plus a trace estimate by Figalli, Maggi and Pratelli.

scholarly journals On the area of planar convex sets containing many lattice points

1987 ◽  
Vol 35 (3) ◽  
pp. 441-454
Author(s):  
P. R. Scott
Keyword(s):  
Large Class ◽  
Convex Sets ◽  
Convex Set ◽  
Lattice Points ◽  
Symmetric Convex ◽  
Classical Theorem ◽  
Planar Convex

A classical theorem of van der Corput gives a bound for the volume of a symmetric convex set in terms of the number of lattice points it contains. This theorem is here generalized and extended for a large class of non-symmetric sets in the plane.

COMPUTATIONAL ASPECTS OF HELLY’S THEOREM AND ITS RELATIVES

1995 ◽  
Vol 05 (04) ◽  
pp. 357-367 ◽  
Author(s):  
DAVID AVIS ◽  
MICHAEL E. HOULE
Keyword(s):  
Time Complexity ◽  
Convex Sets ◽  
Space Complexity ◽  
Convexity Theorem ◽  
Helly’S Theorem ◽  
Multi Stage ◽  
Common Intersection ◽  
Computational Aspects ◽  
The Common ◽  
Time Required

This paper investigates computational aspects of the well-known convexity theorem due to Helly, which states that the existence of a point in the common intersection of n convex sets is guaranteed by the existence of points in the common intersection of each combination of d+1 of these sets. Given an oracle which accepts d+1 convex sets and either returns a point in their common intersection, or reports its non-existence, we give two algorithms which compute a point in the common intersection of n such gets. The first algorithm runs in O(nd+1T) time and O(nd) space, where T is the time required for a single call to the oracle. The second algorithm is a multi-stage variant of the first by which the space complexity may be reduced to O(n) at the expense of an increase in the time complexity by a factor independent of n. We also show how these algorithms may be adapted to construct linear and spherical separators of a collection of sets, and to construct a translate of a given object which either contains, is contained by, or intersects a collection of convex sets.

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